The exemplary embodiment relates to question answering (Q&A) systems and finds particular application in connection with a system and method for tailoring a Q&A system to find appropriate results for user requests.
Customer relation centers provide users with assistance when they face problems with a device or service, either by electronic communication or by telephone interaction with an agent. A knowledge base, which includes known solutions to problems that a user may face, may be accessed by the agent or the user to search for an answer to a user's request. Using human agents, however, is costly and thus attempts have been made to automate the process, where possible. Automated question answering systems offer a way to facilitate analysis of the content of requests submitted by end-users. They can provide several benefits, such as facilitating the identification of the problem or topic being discussed to help the agent find the most appropriated answer, automating part of the response and therefore reducing the task for the agent, and providing analysis of the interaction in order to improve future customer interactions and general customer satisfaction.
Customers and other end-users are often unfamiliar with the content and structure of the knowledge base and thus interaction with an automated system using natural language queries is often used. However, relevant answers are often not retrieved through such queries. This can be because the amount of information provided by the user in his request is not sufficient or because the nature of the information that is sought is unclear. For example, a request that results from a lack of knowledge by the user seeks different information from a request concerning a problem relating to a device malfunction.
Automated systems are generally based either on keyword matching or pattern matching. Several methods for improving searching inside a knowledge base to find the best match between a query and the most appropriate answer have been proposed. See, for example, Andrenucci, A., “Automated Question Answering: Review of the Main Approaches,” Proc. 3rd Intern'l Conf. on Information Technology and Applications, pp. 514-519 (2005). Natural Language Processing (NLP) and machine learning have been used to learn how to recognize frequent types of questions. In pattern matching, the system looks for very specific combinations of words to trigger corresponding responses. Machine learning techniques use categorization models to find the best match between the words contained in a query and the typical vocabulary of previously learned classes of problems. One method is based on direct keyword matching, enhanced by the use of categorization algorithms to rank answers according to the frequency of the used vocabulary. Another approach uses machine learning to create clusters of queries related to a same answers in order to capture the typical vocabulary used to describe a given problem. Then, finding the category of questions allows the system to identify a related problem. Refinements to these approaches involve the use of synonyms or derivational morphology to enlarge the lexical scope of a query to increase the likelihood of finding a match with the content of the knowledge base.
Problems remain with these approaches. Pattern matching triggers rules or responses only when some specific sequence or combination of words is detected. As a result, a low recall can occur since it is not usually feasible to anticipate all possible queries. Machine learning can offer a higher recall as the mapping is often performed using a likelihood measure, but a drawback with this approach is a lack of real understanding of the meaning of the query. The words used and their frequency are taken into account but not their order and how they interact altogether. As an example, the following two queries with similar words may be placed in the same problem category even if the real meaning, as it relates to the underlying problem is not the same:
1. I cannot use Google maps after installing it.
2. I cannot install Google Maps and therefore am not able to use it.
In the first query, the problem relates to a lack of knowledge. The device may well be functioning correctly but the user lacks information on how to use it. In the second query, the problem may be related to the installation of the application. Thus, even if the user knows how to use the application, he cannot do so until the application is installed by the device. In such a case, standard systems based only on word frequency will suggest, among the possible solutions, some that are unlikely to solve the user's problem. This may cause frustration if it suggests that there is a lack of understanding of the user's needs and negatively impact the customer relationship.
There remains a need for a system and method capable of analyzing a user's request in order to create a differential search in the knowledge base.